Low-power Programmable Processor for Fast Fourier Transform Based on Transport Triggered Architecture

This paper describes a low-power processor tailored for fast Fourier transform computations where transport triggering template is exploited. The processor is software-programmable while retaining an energy-efficiency comparable to existing fixed-function implementations. The power savings are achieved by compressing the computation kernel into one instruction word. The word is stored in an instruction loop buffer, which is more power-efficient than regular instruction memory storage. The processor supports all power-of-two FFT sizes from 64 to 16384 and given 1 mJ of energy, it can compute 20916 transforms of size 1024.Comment: 5 pages, 4 figures, 1 table, ICASSP 2019 conferenc

Fast Fourier transforms on energy-efficient application-specific processors

Many of the current applications used in battery powered devices are from digital signal processing, telecommunication, and multimedia domains. Traditionally application-specific fixed-function circuits have been used in these designs in form of application-specific integrated circuits (ASIC) to reach the required performance and energy-efficiency. The complexity of these applications has increased over the years, thus the design complexity has increased even faster, which implies increased design time. At the same time, there are more and more standards to be supported, thus using optimised fixed-function implementations for all the functions in all the standards is impractical. The non-recurring engineering costs for integrated circuits have also increased significantly, so manufacturers can only afford fewer chip iterations. Although tailoring the circuit for a specific application provides the best performance and/or energy-efficiency, such approach lacks flexibility. E.g., if an error is found after the manufacturing, an expensive chip iteration is required. In addition, new functionalities cannot be added afterwards to support evolution of standards. Flexibility can be obtained with software based implementation technologies. Unfortunately, general-purpose processors do not provide the energy-efficiency of the fixed-function circuit designs. A useful trade-off between flexibility and performance is implementation based on application-specific processors (ASP) where programmability provides the flexibility and computational resources customised for the given application provide the performance. In this Thesis, application-specific processors are considered by using fast Fourier transform as the representative algorithm. The architectural template used here is transport triggered architecture (TTA) which resembles very long instruction word machines but the operand execution resembles data flow machines rather than traditional operand triggering. The developed TTA processors exploit inherent parallelism of the application. In addition, several characteristics of the application have been identified and those are exploited by developing customised functional units for speeding up the execution. Several customisations are proposed for the data path of the processor but it is also important to match the memory bandwidth to the computation speed. This calls for a memory organisation supporting parallel memory accesses. The proposed optimisations have been used to improve the energy-efficiency of the processor and experiments show that a programmable solution can have energy-efficiency comparable to fixed-function ASIC designs

Design and synthesis of a high-performance, hyper-programmable DSP on an FPGA

In the field of high performance digital signal processing, DSPs and FPGAs provide the most flexibility. Due to the extensive customization available on FPGAs, DSP algorithm implementation on an FPGA exhibits an increased development time over programming a processor. Because of this, traditional DSPs typically yield a faster time to market than an FPGA design. However, it is often desirable to have the ASIC-like performance that is attainable through the additional customization and parallel computation available through an FPGA. This can be achieved through the class of processors known as hyper-programmable DSPs. A hyper-programmable DSP is a DSP in which multiple aspects of the architecture are programmable. This thesis contributes such a DSP, targeted for high-performance and realized in hardware using an FPGA. The design consists of both a scalar datapath and a vector datapath capable of parallel operations, both of which are extensively customizable. To aid in the design of the datapaths, graphical tools are introduced as an efficient way to modify the design. A tool was also created to supply a graphical interface to help write instructions for the vector datapath. Additionally, an adaptive assembler was created to convert assembly programs to machine code for any datapath design. The resulting design was synthesized for a Cyclone III FPGA. The synthesis resulted in a design capable of running at 135MHz with 61% of the logic used by processing elements. Benchmarks were run on the design to evaluate its performance. The benchmarks showed similar performance between the proposed design and commercial DSPs for the simple benchmarks but significant improvement for the more complex ones

Enabling virtual radio functions on software defined radio for future wireless networks

Today's wired networks have become highly flexible, thanks to the fact that an increasing number of functionalities are realized by software rather than dedicated hardware. This trend is still in its early stages for wireless networks, but it has the potential to improve the network's flexibility and resource utilization regarding both the abundant computational resources and the scarce radio spectrum resources. In this work we provide an overview of the enabling technologies for network reconfiguration, such as Network Function Virtualization, Software Defined Networking, and Software Defined Radio. We review frequently used terminology such as softwarization, virtualization, and orchestration, and how these concepts apply to wireless networks. We introduce the concept of Virtual Radio Function, and illustrate how softwarized/virtualized radio functions can be placed and initialized at runtime, allowing radio access technologies and spectrum allocation schemes to be formed dynamically. Finally we focus on embedded Software-Defined Radio as an end device, and illustrate how to realize the placement, initialization and configuration of virtual radio functions on such kind of devices

Ultra-Low Leakage, Energy-Efficient Digital Integrated Circuit and System Design

The advances of the complementary metal-oxide-semiconductor (CMOS) technology manufacturing and design over the years have enabled a diverse range of applications across the power consumption, performance, and area (PPA) spectra. Many of the recent and prospective applications rely on the availability of energy-autonomous, miniaturized systems, i.e., ultra-low power (ULP) VLSI systems, which are generally characterized by extreme resource limitations. Some examples of applications are wireless sensing platforms, body-area sensor networks (BASN), biomedical and implantable devices, wearables, hearables, and monitors. Within the context of such applications, the key requirements are long lifetime and miniaturized size (sub-/millimeter-scale). In order to enable both requirements, energy-efficiency is the key metric. It allows for extended battery lifetime and operation with the energy that can be harvested from the environment, and it limits the size (volume) of the energy sources utilized to power these systems. Ultra-low voltage (ULV) operation is a key technique in which the VDD of circuits is reduced from nominal to near or below the threshold voltage of the transistor. It is a powerful knob that has been largely exploited by designers in order to achieve ultra-low power consumption and high energy-efficiency in CMOS. Existing ULP VLSI systems typically operate at a lower supply voltage thereby reducing their energy consumption by one to two orders of magnitude in order to enable the aforementioned applications. While supply voltage scaling is a promising measure for achieving low power and reducing energy consumption, it brings up several challenges. One critical issue is the leakage energy dissipated by the devices, which is magnified in portion to the total energy consumption at ULV. The reason is that, as VDD scales from nominal to near-threshold and sub-threshold, transistors become increasingly slower and they accumulate more leakage (i.e., static) power over longer cycle times. This energy waste accounts for a significant portion of the system's total energy consumption, offsets the gains provided by voltage scaling, defines the minimum energy per operation, and poses a practical limit for the system's energy-efficiency. This thesis presents selected research works on ultra-low leakage, energy-efficient digital integrated circuit design. More specifically, it describes novel and key techniques for minimizing the energy waste of idle/underutilized and always-on hardware. The main goal of such techniques is to push the envelope of energy-efficiency in energy-autonomous, miniaturized VLSI systems. Such techniques are applied to key building blocks of emerging mobile and embedded computing devices resulting in state-of-the-art energy-efficiencies

Sovelluskohtainen käskykantaprosessori tulevaisuuden radiomikropiireihin

Licensed Assisted Access is a 3GPP specified feature, for using the unlicensed frequen-cy band as a supplemental transmission medium to the licensed band. LAA uses clear channel assessment, for discovering the channel state and accessing the medium. LAA provides a contention based algorithm, featuring a conservative listen-before-talk scheme, and random back-off. This CCA scheme is thought to increase co-existence with existing technologies in the unlicensed band, namely, WLAN and Bluetooth. Application-specific instruction-set processors can be tailored to fit most applications, and offer increased flexibility to hardware design through, programmable solutions. ASIP architecture is defined by the designer, while the ASIP tools provide retargetable compiler generation and automatic hardware description generation, for faster design exploration. In this thesis, we explore the 3GPP LAA downlink requirements, and identify the key processing challenges as FFT, energy detection and carrier state maintenance. To design an efficient ASIP for LAA, we explore the different architectural choices we have available and arrive at a statically scheduled, multi-issue architecture. We evaluate dif-ferent design approaches, and choose a Nokia internal ASIP design as the basis for our solution. We modify the design, to meet our requirements and conclude that the pro-posed solution should fit the LAA use case well

A Study of Hardware Acceleration in System on Chip Designs using Transport Triggered Architecture

Transport Triggered Architecture is a processor design philosophy where the datapath is visible for the programmer and the program controls the data transfers on the path directly. TTA processors offer a good alternative for application specific task as they can be easily optimized for a given application. TTA processors, however, adjust poorly to dynamic situations, but this can be compensated with external hosting. Fast Fourier transform is an approximation of the Fourier transform for converting time domain data into frequency domain. Fast Fourier transform is needed in many digital signal processing applications. One example of the usage of the transform is the LTE network access schemes where the symbols transmitted over the air interface are constructed with the fast Fourier transform and again demodulated as they are received. The study makes use of Nokia Co-Processor as the host for TTA processor and proposes alternatives for different architectures for the usage of the TTA processor inside a practical design where data is being moved over interconnections and memories. One proposed architecture is selected for implementation and the construction of this architecture is discussed regarding implementing the needed hardware and software to run the Fourier application on TTA with data being fetched and written back in system memory. Lastly, the performance of the implementation is discussed

KAVUAKA: a low-power application-specific processor architecture for digital hearing aids

The power consumption of digital hearing aids is very restricted due to their small physical size and the available hardware resources for signal processing are limited. However, there is a demand for more processing performance to make future hearing aids more useful and smarter. Future hearing aids should be able to detect, localize, and recognize target speakers in complex acoustic environments to further improve the speech intelligibility of the individual hearing aid user. Computationally intensive algorithms are required for this task. To maintain acceptable battery life, the hearing aid processing architecture must be highly optimized for extremely low-power consumption and high processing performance.The integration of application-specific instruction-set processors (ASIPs) into hearing aids enables a wide range of architectural customizations to meet the stringent power consumption and performance requirements. In this thesis, the application-specific hearing aid processor KAVUAKA is presented, which is customized and optimized with state-of-the-art hearing aid algorithms such as speaker localization, noise reduction, beamforming algorithms, and speech recognition. Specialized and application-specific instructions are designed and added to the baseline instruction set architecture (ISA). Among the major contributions are a multiply-accumulate (MAC) unit for real- and complex-valued numbers, architectures for power reduction during register accesses, co-processors and a low-latency audio interface. With the proposed MAC architecture, the KAVUAKA processor requires 16 % less cycles for the computation of a 128-point fast Fourier transform (FFT) compared to related programmable digital signal processors. The power consumption during register file accesses is decreased by 6 %to 17 % with isolation and by-pass techniques. The hardware-induced audio latency is 34 %lower compared to related audio interfaces for frame size of 64 samples.The final hearing aid system-on-chip (SoC) with four KAVUAKA processor cores and ten co-processors is integrated as an application-specific integrated circuit (ASIC) using a 40 nm low-power technology. The die size is 3.6 mm2. Each of the processors and co-processors contains individual customizations and hardware features with a varying datapath width between 24-bit to 64-bit. The core area of the 64-bit processor configuration is 0.134 mm2. The processors are organized in two clusters that share memory, an audio interface, co-processors and serial interfaces. The average power consumption at a clock speed of 10 MHz is 2.4 mW for SoC and 0.6 mW for the 64-bit processor.Case studies with four reference hearing aid algorithms are used to present and evaluate the proposed hardware architectures and optimizations. The program code for each processor and co-processor is generated and optimized with evolutionary algorithms for operation merging,instruction scheduling and register allocation. The KAVUAKA processor architecture is com-pared to related processor architectures in terms of processing performance, average power consumption, and silicon area requirements

Design and development from single core reconfigurable accelerators to a heterogeneous accelerator-rich platform

The performance of a platform is evaluated based on its ability to deal with the processing of multiple applications of different nature. In this context, the platform under evaluation can be of homogeneous, heterogeneous or of hybrid architecture. The selection of an architecture type is generally based on the set of different target applications and performance parameters, where the applications can be of serial or parallel nature. The evaluation is normally based on different performance metrics, e.g., resource/area utilization, execution time, power and energy consumption. This process can also include high-level performance metrics, e.g., Operations Per Second (OPS), OPS/Watt, OPS/Hz, Watt/Area etc. An example of architecture selection can be related to a wireless communication system where the processing of computationally-intensive signal-processing algorithms has strict execution-time constraints and in this case, a platform with special-purpose accelerators is relatively more suitable than a typical homogeneous platform. A couple of decades ago, it was expensive to plant many special-purpose accelerators on a chip as the cost per unit area was relatively higher than today. The utilization wall is also becoming a limiting factor in homogeneous multicore scaling which means that all the cores on a platform cannot be operated at their maximum frequency due to a possible thermal meltdown. In this case, some of the processing cores have to be turned-off or to be operated at very low frequencies making most of the part of the chip to stay underutilized. A possible solution lies in the use of heterogeneous multicore platforms where many application-specific cores operate at lower frequencies, therefore reducing power dissipation density and increasing other performance parameters. However, to achieve maximum flexibility in processing, a general-purpose flavor can also be introduced by adding a few Reduced Instruction-Set Computing (RISC) cores. A power class of heterogeneous multicore platforms is an accelerator-rich platform where many application-specific accelerators are loosely connected with each other for work load distribution or to execute the tasks independently. This research work spans from the design and development of three different types of template-based Coarse-Grain Reconfigurable Arrays (CGRAs), i.e., CREMA, AVATAR and SCREMA to a Heterogeneous Accelerator-Rich Platform (HARP). The accelerators generated from the three CGRAs could perform different lengths and types of Fast Fourier Transform (FFT), real and complex Matrix-Vector Multiplication (MVM) algorithms. CREMA and AVATAR were fixed CGRAs with eight and sixteen number of Processing Element (PE) columns, respectively. SCREMA could flex between four, eight, sixteen and thirty two number of PE columns. Many case studies were conducted to evaluate the performance of the reconfigurable accelerators generated from these CGRA templates. All of these CGRAs work in a processor/coprocessor model tightly integrated with a Direct Memory Access (DMA) device. Apart from these platforms, a reconfigurable Application-Specific Instruction-set Processor (rASIP) is also designed, tested for FFT execution under IEEE-802.11n timing constraints and evaluated against a processor/coprocessor model. It was designed by integrating AVATAR generated radix-(2, 4) FFT accelerator into the datapath of a RISC processor. The instruction set of the RISC processor was extended to perform additional operations related to AVATAR. As mentioned earlier, the underutilized part of the chip, now-a-days called Dark Silicon is posing many challenges for the designers. Apart from software optimizations, clock gating, dynamic voltage/frequency scaling and other high-level techniques, one way of dealing with this problem is to use many application-specific cores. In an effort to maximize the number of reconfigurable processing resources on a platform, the accelerator-rich architecture HARP was designed and evaluated in terms of different performance metrics. HARP is constructed on a Network-on-Chip (NoC) of 3x3 nodes where with every node, a CGRA of application-specific size is integrated other than the central node which is attached to a RISC processor. The RISC establishes synchronization between the nodes for data transfer and also performs the supervisory control. While using the NoC as the backbone of communication between the cores, it becomes possible for all the cores to address each other and also perform execution simultaneously and independently of each other. The performance of accelerators generated from CREMA, AVATAR and SCREMA templates were evaluated individually and also when attached to HARP's NoC nodes. The individual CGRAs show promising results in their own capacity but when integrated all together in the framework of HARP, interesting comparisons were established in terms of overall execution times, resource utilization, operating frequencies, power and energy consumption. In evaluating HARP, estimates and measurements were also made in some advanced performance metrics, e.g., in MOPS/mW and MOPS/MHz. The overall research work promotes the idea of heterogeneous accelerator-rich platform as a solution to current problems and future needs of industry and academia

Efficient implementation of channel estimation algorithm for beamforming

Abstract. The future 5G mobile network technology is expected to offer significantly better performance than its predecessors. Improved data rates in conjunction with low latency is believed to enable technological revolutions such as self-driving cars. To achieve faster data rates, MIMO systems can be utilized. These systems enable the use of spatial filtering technique known as beamforming. Beamforming that is based on the preacquired channel matrix is computationally very demanding causing challenges in achieving low latency. By acquiring the channel matrix as efficiently as possible, we can facilitate this challenge. In this thesis we examined the implementation of channel estimation algorithm for beamforming with a digital signal processor specialized in vector computation. We present implementations for different antenna configurations based on three different approaches. The results show that the best performance is achieved by applying the algorithm according to the limitations given by the system and the processor architecture. Although the exploitation of the parallel architecture was proved to be challenging, the implementation of the algorithm would have benefitted from the greater amount of parallelism. The current parallel resources will be a challenge especially in the future as the size of antenna configurations is expected to grow.Keilanmuodostuksen tarvitseman kanavaestimointialgoritmin tehokas toteutus. Tiivistelmä. Tulevan viidennen sukupolven mobiiliverkkoteknologian odotetaan tarjoavan merkittävästi edeltäjäänsä parempaa suorituskykyä. Tämän suorituskyvyn tarjoamat suuret datanopeudet yhdistettynä pieneen latenssiin uskotaan mahdollistavan esimerkiksi itsestään ajavat autot. Suurempien datanopeuksien saavuttamiseksi voidaan hyödyntää monitiekanavassa käytettävää MIMO-systeemiä, joka mahdollistaa keilanmuodostuksena tunnetun spatiaalisen suodatusmenetelmän käytön. Etukäteen hankittuun kanavatilatietoon perustuva keilanmuodostus on laskennallisesti erittäin kallista. Tämä aiheuttaa haasteita verkon pienen latenssivaatimuksen saavuttamisessa. Tässä työssä tutkittiin keilanmuodostukselle tarkoitetun kanavaestimointialgoritmin tehokasta toteutusta hyödyntäen vektorilaskentaan erikoistunutta prosessoriarkkitehtuuria. Työssä esitellään kolmea eri lähestymistapaa hyödyntävät toteutukset eri kokoisille antennikonfiguraatioille. Tuloksista nähdään, että paras suorituskyky saavutetaan sovittamalla algoritmi järjestelmän ja arkkitehtuurin asettamien rajoitusten mukaisesti. Vaikka rinnakkaisarkkitehtuurin hyödyntäminen asetti omat haasteensa, olisi algoritmin toteutus hyötynyt suuremmasta rinnakkaisuuden määrästä. Nykyinen rinnakkaisuuden määrä tulee olemaan haaste erityisesti tulevaisuudessa, sillä antennikonfiguraatioiden koon odotetaan kasvavan